Georgy Maksimovich Adelson-Velsky (obituary)

Арлазаров, Владимир Львович; Dinitz, E A; Ilyashenko, Yu.; Karzanov, Alexander V.; Karpenko, S M; Kirillov, A. A.; Kонстантинов, Никола; Kronrod, M A; Kuznetsov, O. P.; Okun, L.B.; Pevzner, Pavel A.; Semenov, A. L.; Faradzhev, I A; Cherkasskii, B V; Khovanskiĭ, Askold

doi:10.1070/rm2014v069n04abeh004909

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…The second direction includes limiting the functionality of sequence alignment to performing only edit distance calculation, as in Edlib ( Šošić and Šikić 2017 ), or limiting the number of calculated entries in the DP table, as in windowing/tiling the DP table ( Arlazarov et al 1970 , Rizk and Lavenier 2010 , Turakhia et al 2018 ) and the X-drop algorithm ( Zhang et al 2000 ) implemented in one of the versions of KSW2 ( Li 2018 ). This second direction is not mutually exclusive from the first and can also benefit from acceleration via SIMD ( Li 2018 ), GPUs ( Ahmed et al 2020 ), and FPGAs ( Banerjee et al 2018 ).…”

Section: Introductionmentioning

confidence: 99%

A framework for high-throughput sequence alignment using real processing-in-memory systems

et al. 2023

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Motivation Sequence alignment is a memory bound computation whose performance in modern systems is limited by the memory bandwidth bottleneck. Processing-in-memory architectures alleviate this bottleneck by providing the memory with computing competencies. We propose Alignment-in-Memory (AIM), a framework for high-throughput sequence alignment using processing-in-memory, and evaluate it on UPMEM, the first publicly-available general-purpose programmable processing-in-memory system. Results Our evaluation shows that a real processing-in-memory system can substantially outperform server-grade multi-threaded CPU systems running at full-scale when performing sequence alignment for a variety of algorithms, read lengths, and edit distance thresholds. We hope that our findings inspire more work on creating and accelerating bioinformatics algorithms for such real processing-in-memory systems. Availability Our code is available at https://github.com/safaad/aim.

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Section: Introductionmentioning

confidence: 99%

A framework for high-throughput sequence alignment using real processing-in-memory systems

et al. 2023

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New Applied Problems in the Theory of Acyclic Digraphs

Tsitsiashvili

Bulgakov

2021

Mathematics

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The following two optimization problems on acyclic digraph analysis are solved. The first of them consists of determining the minimum (in terms of volume) set of arcs, the removal of which from an acyclic digraph breaks all paths passing through a subset of its vertices. The second problem is to determine the smallest set of arcs, the introduction of which into an acyclic digraph turns it into a strongly connected one. The first problem was solved by reduction to the problem of the maximum flow and the minimum section. The second challenge was solved by calculating the minimum number of input arcs and determining the smallest set of input arcs in terms of the minimum arc coverage of an acyclic digraph. The solution of these problems extends to an arbitrary digraph by isolating the components of cyclic equivalence in it and the arcs between them.

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Georgy Maksimovich Adelson-Velsky (obituary)

Cited by 2 publications

References 3 publications

A framework for high-throughput sequence alignment using real processing-in-memory systems

A framework for high-throughput sequence alignment using real processing-in-memory systems

New Applied Problems in the Theory of Acyclic Digraphs

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